Redistribution effects of water tariffs

Nguyen Bich Ngoc, Jacques Teller 13 December 2021

Household income histogram

inccatdf <- df[!(is.na(df$inccat)),] %>%
  group_by(inccat) %>%
  summarise(count = n(),
            prop = n()/nrow(df),
            income_avr = mean(income),
            income_min = min(income),
            income_max = max(income),
            inceqa_avr = mean(inceqa),
            inceqa_min = min(inceqa),
            inceqa_max = max(inceqa))

inccatdf
## # A tibble: 4 x 9
##   inccat     count   prop income_avr income_min income_max inceqa_avr
##   <fct>      <int>  <dbl>      <dbl>      <int>      <int>      <dbl>
## 1 precarious   148 0.0970      1135.        125       2250      8854.
## 2 modest       697 0.457       1881.       1250       3250     15961.
## 3 average      501 0.329       3203.       2250       4750     23622.
## 4 higher       178 0.117       4739.       3750       5250     30957.
## # ... with 2 more variables: inceqa_min <dbl>, inceqa_max <dbl>

Household income histogram Household income histogram

Utilities Number of households CVD CVA Average price Block 1 price Block 2 price Block2/Block1
SWDE 1138 2.4480 1.745 4.4551 1.2240 4.1930 3.4257
CILE 261 2.6366 1.745 4.6523 1.3183 4.3816 3.3237
IECBW 126 2.1600 1.745 4.0727 1.0800 3.9050 3.6157
## Warning: Removed 192 row(s) containing missing values (geom_path).

## Warning: Removed 150 row(s) containing missing values (geom_path).

Household income quintile characteristics
Quintile Number of households Number of people Min income (EUR/month) Max income (EUR/month)
1 305 474 125 1250
2 305 602 1250 2250
3 305 734 2250 2750
4 305 845 2750 3750
5 305 1006 3750 5250

Income per equivalent adults for different household income group Income per equivalent adults for different household income group

# Correlation between water consumption and household income should use spearman?????

cor.test(df$csmptv, df$income, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  df$csmptv and df$income
## t = 15.505, df = 1523, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3250574 0.4117940
## sample estimates:
##       cor 
## 0.3692295
cor.test(df$csmptv, df$income, method = "spearman")
## Warning in cor.test.default(df$csmptv, df$income, method = "spearman"):
## Cannot compute exact p-value with ties

## 
##  Spearman's rank correlation rho
## 
## data:  df$csmptv and df$income
## S = 358665135, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.3932203
# Correlation between water consumption and income per equivalent adult should use spearman?????

cor.test(df$csmptv, df$inceqa, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  df$csmptv and df$inceqa
## t = 1.8473, df = 1523, p-value = 0.06489
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.00291998  0.09724963
## sample estimates:
##       cor 
## 0.0472837
cor.test(df$csmptv, df$inceqa, method = "spearman")
## Warning in cor.test.default(df$csmptv, df$inceqa, method = "spearman"):
## Cannot compute exact p-value with ties

## 
##  Spearman's rank correlation rho
## 
## data:  df$csmptv and df$inceqa
## S = 547594684, p-value = 0.004034
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.07359449

Proportion of household paying in which block by quantile Proportion of household paying in which block by income quintile and utilities

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing non-finite values (stat_summary).

## Warning: Removed 9 rows containing non-finite values (stat_summary).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing non-finite values (stat_summary).

## Warning: Removed 9 rows containing non-finite values (stat_summary).

summary(df$avrprc)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.955   4.324   4.419   4.910   4.576  20.055
summary(df$avrprc[df$poorest == 1])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   4.076   4.416   4.551   5.606   5.499  16.277
summary(df$avrprc[df$inccat == "precarious"])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   3.971   4.332   4.457   5.098   4.665  16.277       1
summary(df$subs[df$inccat == "precarious"])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## -79.564  -4.286   1.840  -3.918  10.142  30.484       1
summary(df$mgnprc)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.080   4.193   4.193   3.693   4.193   4.382
summary(df$mgnprc[df$poorest == 1])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.080   1.224   4.193   3.155   4.193   4.382
summary(df$mgnprc[df$inccat == "precarious"])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.080   3.905   4.193   3.478   4.193   4.382       1
## 3.5. changing fixed  -----

### new cvd ------

Scenarios

Fixed

CVD

CVA

SWDE CILE IECBW
As in 2014 101.4797 2.4480 2.6366 2.1600 1.745
1 0.0000 4.3356 4.5642 3.7129 1.745
2 50.0000 3.4040 3.6470 2.9003 1.745
3 100.0000 2.4724 2.7298 2.0877 1.745
4 150.0000 1.5408 1.8126 1.2751 1.745
5 200.0000 0.6092 0.8954 0.4625 1.745

Rainwater tank tax

Averaged Fixed

CVD

CVA

SWDE CILE IECBW
0 101.1914 2.4480 2.6366 2.1600 1.745
50 95.4887 2.1349 2.4669 1.8680 1.745
100 89.7860 1.8218 2.2972 1.5759 1.745
150 84.0834 1.5087 2.1276 1.2839 1.745
200 78.3807 1.1956 1.9579 0.9919 1.745

SWDE CILE IECBW fixed revincr
4.455142 4.652346 4.072663 0 0.0
3.717046 3.923825 3.416345 50 0.0
2.978950 3.195304 2.760027 100 0.0
5.346171 5.582816 4.887196 0 0.2
4.608075 4.854294 4.230878 50 0.2
3.869979 4.125773 3.574559 100 0.2
6.682713 6.978520 6.108995 0 0.5
5.944617 6.249999 5.452677 50 0.5
5.206522 5.521477 4.796358 100 0.5
SWDE CILE IECBW fixed revincr
1.809970 1.882461 1.604395 0 0.0
1.510107 1.587682 1.345843 50 0.0
1.210244 1.292903 1.087292 100 0.0
2.171964 2.258953 1.925274 0 0.2
1.872101 1.964174 1.666723 50 0.2
1.572239 1.669396 1.408171 100 0.2
2.714955 2.823691 2.406593 0 0.5
2.415092 2.528913 2.148041 50 0.5
2.115229 2.234134 1.889489 100 0.5
SWDE CILE IECBW fixed revincr
1.839406 1.875260 1.611007 0 0.0
1.534667 1.581609 1.351390 50 0.0
1.229927 1.287958 1.091772 100 0.0
2.207288 2.250312 1.933208 0 0.2
1.902548 1.956661 1.673591 50 0.2
1.597808 1.663010 1.413974 100 0.2
2.759110 2.812890 2.416510 0 0.5
2.454370 2.519239 2.156893 50 0.5
2.149630 2.225588 1.897276 100 0.5